Carnegie Mellon Team A Finalist In the Elsevier Grand Challenge

Byron Spice Monday, January 12, 2009

A Carnegie Mellon University team of experts in computational biology and machine learning is among four finalists in the inaugural Elsevier Grand Challenge on Knowledge Enhancement in the Life Sciences, a contest designed to encourage development of tools dealing with the ever increasing amount of online life sciences information.

Their entry, the Structured Literature Image Finder (SLIF), is based on a project called the Subcellular Location Image Finder that seeks out fluorescence microscope images in on-line journal articles, and indexes them according to cell line, proteins visualized, and resolution. The images then can be accessed via a Web database.

The seven member SLIF team includes leaders Robert F. Murphy, the Ray and Stephanie Lane professor of computational biology, biological sciences, biomedical engineering and machine learning; William Cohen, associate professor of machine learning, and Eric Xing, assistant professor in machine learning, language technologies and computer science, as well as graduate students Amr Ahmed, Andrew Arnold, Luis Pedro Coelho, and Josh Kangas and research programmer Saboor Sheikh.

Ahmed is in the Ph.D. program of the Language Technologies Institute, Arnold is in the Machine Learning Department Ph.D. program, and Coelho and Kangas are in the Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program in Computational Biology. The group competed against more than 70 teams to capture one of the top four positions.

The SLIF team and the three other finalists will present papers on the technologies they've developed in a public symposium at "Experimental Biology 2009," which takes place April 18-22 in New Orleans. There the judges will announce the contest winners during a live webnar. The team taking first place will be awarded a cash prize of US $35,000 and the second place winner a cash prize of US$15,000.

The Subcellular Location Image Finder was the first system to extract information from both text and images in biological journal articles. Its continued development is supported by a grant from the National Institutes of Health.